Nicola Rieke
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 10%
- Health Informatics top 2%
- Biomedical Engineering
- Co-authors
- M. Jorge CardosoFausto MilletarìDaguang XuWenqi LiYan ChengWentao ZhuSébastien OurselinMaximilian Baust
- Topics
- Artificial Intelligence in Healthcare and Education (2 papers)Medical Image Segmentation Techniques (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)
- Journals
- Investigative Ophthalmology & Visual ScienceMedical Image AnalysisLecture notes in computer science
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Nicola Rieke
11 papers receiving 440 citations
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 326
- Radiology, Nuclear Medicine and Imaging 149
- Computer Vision and Pattern Recognition 90
- Health Informatics 55
- Biomedical Engineering 38
Countries citing papers authored by Nicola Rieke
This map shows the geographic impact of Nicola Rieke's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicola Rieke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Rieke more than expected).
Fields of papers citing papers by Nicola Rieke
This network shows the impact of papers produced by Nicola Rieke. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicola Rieke. The network helps show where Nicola Rieke may publish in the future.
Co-authorship network of co-authors of Nicola Rieke
This figure shows the co-authorship network connecting the top 25 collaborators of Nicola Rieke. A scholar is included among the top collaborators of Nicola Rieke based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nicola Rieke. Nicola Rieke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 17 | |
| 4 | 1 | |
| 5 | 42 | |
| 6 | Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings | 4 |
| 7 | 294 | |
| 8 | 63 | |
| 9 | Injection Assistance via Surgical Needle Guidance using Microscope-Integrated OCT (MI-OCT) | 2 |
| 10 | 27 | |
| 11 | 1 |
About Nicola Rieke
Nicola Rieke is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Emergency Medicine, having authored 11 papers that have together received 454 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (2 papers), Medical Image Segmentation Techniques (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Health Informatics (55 citations), Artificial Intelligence (326 citations) and Radiology, Nuclear Medicine and Imaging (149 citations). Nicola Rieke has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include M. Jorge Cardoso, Fausto Milletarì, Daguang Xu, Wenqi Li, Yan Cheng, Wentao Zhu, Sébastien Ourselin, Maximilian Baust, Andrew Feng and Shadi Albarqouni. Their work appears in journals such as Investigative Ophthalmology & Visual Science, Medical Image Analysis and Lecture notes in computer science.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.